Ratio planning (also known as attach rate planning) is the process of creating a forecast for a component by recognizing a relationship between the component and its parent. Ratio planning is a common practice in the computer industry and is used in other industries as well. For example, a computer manufacturer may offer a menu of sales models ranging from entry level to high end. The manufacturer will typically produce a unit demand forecast for these models. Within each model, the manufacturer will also offer various feature choices such as processor speeds, memory sizes, and graphics sizes. By analyzing historical sales data as well as current market and technology trends, the manufacturer will establish an average usage ratio between the model (the parent) and the various features (the components). The unit demand forecast of each model is then multiplied by the feature ratios of that model to produce a demand forecast for the features. A computer manufacturer may have hundreds of models to select from with each model having thousands of unique features. The combinations of features to models could number in the tens of thousands or more.
Currently, the type of data required for ratio planning may come from a variety of sources and locations. The required data may be stored in separate, disconnected databases. Sales data, for example, may be stored in a logistics system while unit costs may be stored in a financial system. In addition, some values specific to ratio planning may require derivation based on existing data. For example, feature to model sales ratios may be calculated from sales data by dividing feature to model unit sales by model unit sales. Finally, the required data may be sourced from multiple corporate locations around the globe. The nomenclature for model and feature may be different at these different locations. Because of the large amounts of data that must be gathered, often from disparate sources and in less than desirable formats, it would be advantageous to have an automated system capable of collecting and correlating ratio data in a quick and accurate manner.
Today, ratio planning data is often stored in a nomenclature that is not conducive to human interpretation. Features are typically represented by a unique numeric or alphanumeric part number. This part number is often random and provides no obvious clue as to the feature that it represents. It would be advantageous to have an easy way to tie part numbers to attributes such as family and commodity.
Ratio planning requires that vast amounts of related data be presented to planners in a coherent, concise and speedy fashion. To perform ratio planning, the planner requires data such as sales, planning and order backlog data in both unit and ratio forms. Additional data, such as statistical values like the mean, may also be valuable in ratio planning. Along with requiring particular data fields, the planner requires an efficient method to sort through the possible tens of thousands of feature to machine combinations to quickly identify ratios that are of interest to the particular planner. The criteria the planner uses to identify feature ratios of interest may be those that he plans or those with high dollar output.
Performing ratio planning typically involves additional functions beyond planning. These additional functions could include error checks, data summarizations, and forecast accuracy measurements as well as answering numerous unique, ad-hoc questions. It would be advantageous to provide an automated method that would improve the overall efficiency and accuracy of ratio planning.